Difference between revisions of "ANLY482 AY2016-17 T1 Group2: Project Overview"

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<div style="background:#2196F3; line-height:0.3em; font-family:sans-serif; font-size:120%; border-left:#BBDEFB solid 15px;"><div style="border-left:#FFFFFF solid 5px; padding:15px;"><font color="#FFFFFF"><strong>Motivation</strong></font></div></div>
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At Singapore Pools, the demands of sports matches are anticipated through experience and gut feeling of its Sports Division staffs, which human resources (telephone betting staffs) are then allocated based on the turnout or demand of individual soccer match. Such an approach can prove to be erratic at times, as humans can be very prone to errors and/or other factors, which will negatively impact their decision making capabilities. Any errors or failures in making the right decision can also prove to be costly for Singapore Pools, as they will not be able cope with the demands and these customers will potentially resort to illegal counterparts to place their bets.
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As TixCo is the only authorised ticketing service provider for certain events, it is crucial for them to cater to the demand of the mass public. Currently, TixCo is unable to anticipate the demand for the event organised and this post challenge for TixCo to fully capture the demand efficiently. As such, any uncaptured demand is an opportunity lost for TixCo. Hence, it is important for them to understanding the demand for an event.
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For this, TixCo will need to:
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* Understand the trend and pattern of the number of tickets sold per event
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* Identifying the possible bottleneck for demand
 
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We aim to build a Sports Event Demand Prediction Model, where Singapore Pools will be able to predict demand for individual sports matches from different leagues based on historic data.
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The objective of this project is to:
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* Examine the underlying factors that affect the number of tickets sold per event
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* Understanding the distribution of the number of tickets sold per event
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* Understanding the relationship between the other attributes and the number of tickets sold per event
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The project team also aims to develop an appropriate model to predict the number of tickets sold per event, based on the historical data provided by TixCo.
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The Sports Event Demand Prediction should ultimately allow Singapore Pools to achieve the following:
 
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# More accurate budgeting for the Sports Division
 
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# Improvements in resources allocation depending on various match parameters, such as kick off time, day, league and influence of the soccer team
 
  
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Singapore Pools is also keen and hopeful of using the Sports Event Demand Prediction Model as a foundation for future sports-related prediction models.
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The datasets provided by TixCo are:
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# Profit & Loss (P&L) records
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# Timetables of the events serviced
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# Types of events
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The data are presented in the form of Microsoft Excel worksheets and contains records over the time span of more than 6 years (Jan 2010 to May 2016).
 
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Latest revision as of 08:01, 16 October 2016

Home

Team

Project Overview

Project Findings

Project Management

Documentation


Business Problem & Motivation

As TixCo is the only authorised ticketing service provider for certain events, it is crucial for them to cater to the demand of the mass public. Currently, TixCo is unable to anticipate the demand for the event organised and this post challenge for TixCo to fully capture the demand efficiently. As such, any uncaptured demand is an opportunity lost for TixCo. Hence, it is important for them to understanding the demand for an event.

For this, TixCo will need to:

  • Understand the trend and pattern of the number of tickets sold per event
  • Identifying the possible bottleneck for demand


Project Objective

The objective of this project is to:

  • Examine the underlying factors that affect the number of tickets sold per event
  • Understanding the distribution of the number of tickets sold per event
  • Understanding the relationship between the other attributes and the number of tickets sold per event

The project team also aims to develop an appropriate model to predict the number of tickets sold per event, based on the historical data provided by TixCo.


Datasets

The datasets provided by TixCo are:

  1. Profit & Loss (P&L) records
  2. Timetables of the events serviced
  3. Types of events

The data are presented in the form of Microsoft Excel worksheets and contains records over the time span of more than 6 years (Jan 2010 to May 2016).

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